SCAT3 Symptom Reporting and Screening for Mental Health Disorders in Student-Athletes
Bibliographic record
Abstract
Athlete mental health disorders have gained increasing attention over the past two decades, yet accurate prevalence rates of depression and anxiety remain unclear due to inconsistent terminology, reporting biases, and reliance on self-report measures. In contrast, sport-related concussion (SRC) protocols, including mandatory baseline assessments such as the Sport Concussion Assessment Tool (SCAT), are well established across sport organizations. This dissertation explored the feasibility of hybridizing baseline SRC tools as mental health screeners for post-secondary athletes. Using data from the Active Rehabilitation study, a multi-site international project, baseline assessments from 1,638 Canadian U Sports and U.S. NCAA athletes were analyzed. Predictor variables included the four SCAT3 mood items (more emotional, irritability, sadness, nervousness/anxious), while outcome variables were depression and anxiety symptom severity measured by the Brief Symptom Inventory-18 (BSI-18). Bivariate correlations demonstrated significant positive associations between all SCAT3 mood items and both BSI-18 depression and anxiety subscales, independent of demographic factors such as sex and concussion history. Stepwise multiple regression analyses revealed that sadness, nervousness/anxious, and irritability were the strongest predictors of depression, while nervousness/anxious and more emotional were the strongest predictors of anxiety. Binary logistic regression analyses further showed that SCAT3 items, particularly nervousness/anxious, significantly increased the odds of meeting clinical thresholds for depression and anxiety. These findings suggest that routinely administered SCAT mood items have utility as preliminary screeners for mental health risk, particularly for anxiety symptoms, in post-secondary athletes. This hybridized approach may advance preventive, data-driven, and athlete-centred care in high-performance sport.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".